arXiv (All)
open access
HiPAD: A Unified Gait, Audio-Visual, and Personality Fusion Network for Depression Detection, with a Hierarchical Personality Ablation and the Calibration Limits of Domain-Adapted LLMs
62p1068t07computation-and-languagecomputer-vision-and-pattern-recognitioni-2-6i-5-4j-3machine-learning
We present HiPAD, a single fusion network submitted to all six sub-tracks of the MPDD-AVG 2026 depression-detection challenge, covering the Elder and Young cohorts and three modality sets (gait, audio-visual, and both), each combined with personality descriptions. The network encodes each available
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Differentiation between depressive disorder and insomnia disorder with depressive symptoms across age groups using machine learning.#653379
- Mental health, personality, and cross-addictions as predictors of social media addiction: a machine learning longitudinal study.#331296
- Supplementary Material for: Using Machine Learning with the Brief Symptom Inventory to Screen for Comorbid Addictions: A Proof-of-Principle Study#353657
- Validating the Chronic Stress Indicator: A Data-Driven Framework for Integrating Physiological and Socio-Behavioral Measures.#285219
- Can speech reveal mental health status? A study of linguistic features across multiple samples of at-risk adults.#360896
Record · ID 1325857
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive